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Web mining for better web usability

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Show simple item record Mostafiz, Golam 2010-10-13T03:46:09Z 2010-10-13T03:46:09Z 2007-12
dc.identifier.other ID 03201078
dc.description This thesis paper is submitted in a partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering , BRAC University en_US
dc.description.abstract To develop more effective user-oriented learning techniques for the Web, we need to be able to identify a meaningful session unit from which we can learn. Without this, we could have a high risk to mix up the different user’s activity in the web. We are interested to detect boundaries of sequences between related sessions that would group the activities for a learning purpose. But identification of user session is not always easy where logged on and cookie information is not available. The problem of predicting user access in web pages has recently gets a significant attention. Several algorithms have been proposed, which find important applications, like user profiling, web perfecting, design of adaptive web sites, etc. In all these applications the main issue is the development of an effective prediction system. Because of its importance in reducing user perceived latency present in every Web-based application, which is a usability issue. This thesis paper describes a data mining technique for identify user sessions from huge amount of web log data and a web system, which makes prediction about the user target page by using those sessions to guide the user in World Wide Web. en_US
dc.publisher School of Engineering and Computer Science (SECS) , BRAC University en_US
dc.title Web mining for better web usability en_US
dc.type Thesis en_US

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